When Local Classifiers Become Representations: The Plasticity Tax of Unsupported Outputs

In class-incremental local learning, classifier outputs can become downstream representations before all classes are observed. Outputs for classes without positive examples remain unsupported, yet participate in local softmax competition and receive updates. We investigate the representation-level plasticity tax through matched interventions on unsupported-output count, competition strength, exposure depth, and training phase, holding capacity, training data, replay, and optimization budgets fixed. Local softmax analysis motivates Anchor-Forward, an exact intervention on the unsupported-coordinate update path. At a common input and model state, it preserves forward computation and supported-logit gradients while blocking direct local gradients through unsupported coordinates. In DeeperForward, depth and phase interventions identify the largest losses in shallow layers and during new-task acquisition. On CIFAR-100, Anchor-Forward improves arriving-task accuracy by 27.66 percentage points over matched competition in a two-task experiment with 80 unsupported outputs. In a ten-task stream, it recovers 16.13 points in mean arriving-task accuracy. Experiments with HCL-FF and GTSRB demonstrate plasticity recovery across architectures and domains. These findings indicate that output support is part of representation design when local classifier responses are propagated to subsequent learners.